Digital platform to identify health conditions and therapeutic interventions using an automatic and distributed artificial intelligence system

ABSTRACT

This disclosure is directed to method and system for automatic, distributed, computer-aided, and intelligent data collection/analytics, health monitoring, health condition identification, and patient preventive/remedial health advocacy. The system integrates (1) distributed patient health data collection devices, (2) centralized or distributed data servers running various intelligent and predictive data analytics engines for health screening, assessment, patient health condition identification, and patient preventive/remedial health advocacy, 3) specifically designed data structures including quantized health indicator vectors, patient health condition identification matrices and patient health condition vectors, (4) portal servers configured to interface with (5) distributed physician terminal devices and (6) distributed patient terminal devices for delivering health condition identification, health interventions and patient preventive/remedial health advocacy, and for monitoring and tracking patient activities. The various intelligent and predictive engines are configured to learn and extract hidden features and correlations from a large amount of data obtained from the distributed data collection devices.

TECHNICAL FIELD

This disclosure relates to an automated and distributed platform forcomputer-aided health screening, health risk assessment, diseaseintervention, patient health condition identification, health advocacy,and health monitoring.

BACKGROUND

Many healthcare providers rely on visual interpretation of a patientduring a health assessment. For example, identifying abnormal posture orcentral adiposity are often assessed visually. It is important todistinguish between types of abnormal posture, such as scoliosis, pelvictwists, or lower cross syndrome for predicting health risks,prognostication, and effective intervention/therapy matching. Patienthealth data (PHD) is ordinarily collected and processed onsite incentralized medical centers, hospitals, clinics, and medical labs. Thecollected data are ported to electronic medical record systems to beexamined and analyzed by physicians and other medical professionals forfurther health screening, health risk assessment, disease prevention,patient health condition identification (PHCI), and patientpreventive/remedial health advocacy (PPRHA). Patient preventive/remedialhealthy advocacy may be alternatively referred to as patient therapeuticinterventions. The term “therapeutic” is used herein to broadly refer toprescriptive or nonprescriptive medicine, supplements, self-directedmanagement, at-home care, therapies, medical/biological tests,referrals, and the like based on the patient health conditions. Often,patient health data collection and health assessment require in-personclinic or hospital visits by patients. Visual assessment of the humanbody carries inconsistency, lack of reproducibility, and requires accessto a healthcare provider, which is not always possible in rural orunderserved populations and often produce findings that can beinconsistent and difficult to reproduce. Alternatively, takingmeasurements by hand using measuring tape and a goniometer providesobjective data, but is a very time consuming process. However, the sameissues of inconsistency, lack of reproducibility, and requirements ofaccess to a healthcare provider remain the same using handheld measuringtools. Additionally, manual anthropometric measurements such asmeasurements of girth or posture measurements have been shown to vary inprecision and have poor inter and intra-actor reliability.

SUMMARY

This disclosure describes an automatic, distributed, computer-aided, andintelligent system and platform for health monitoring and datacollection/analytics. The system integrates (1) distributed PHDcollection servers and devices, (2) centralized or distributed dataservers running various intelligent and predictive data analyticsengines for health screening, risk assessment, PHCI and PPRHA, (3)specifically designed data structures including quantized healthindicator vectors, a quantized PHCI matrix, and patient health conditionvectors, (4) portal servers configured to interface with (5) distributedphysician terminal devices, and (6) distributed patient terminal devicesfor receiving health evaluations, delivering interventions and PPRHAitems, and for monitoring and tracking patient activities. The systemdisclosed herein is based on computer technologies and designed to useartificial intelligence tools to solve technical problems incomputer-aided health screening, risk assessment, PHCI, and PPRHA.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary distributed data processing system forautomatic and intelligent PHD collection/analytics, health screening,risk assessment, disease intervention, PHCI, PPRHA, and patientmonitoring.

FIG. 2 illustrates an exemplary data workflow for the distributed dataprocessing system of FIG. 1.

FIG. 3 illustrates an exemplary distributed PHD collection device in theform of a 3D body scanner for producing topographical data in a form of3D body mesh scan.

FIG. 4 shows exemplary electronic components of the 3D topographicalbody scanner of FIG. 3.

FIG. 5 illustrates data flow for processing and transforming digitalinformation collected from the 3D topographical body scanner of FIGS. 3and 4 in accordance with the distributed and intelligent data processingsystem of FIGS. 1 and 2.

FIG. 6 illustrates an exemplary reference frame for 3D body topographydata for two different exemplary body positions.

FIG. 7 illustrates 3D body topography data normalized to the referenceframe of FIG. 6 and shown in various perspectives and views for onerepresentative body position.

FIG. 8 illustrates identification of body landmarks and theirrepresentations, and determination of static and dynamic balanceattributes of a patient from the 3D body topography data for onerepresentative body position.

FIG. 9 illustrates computer-aided circumferential measurements based on3D body topography data for one representative body position.

FIG. 10 shows exemplary data describing body circumferences with centerof mass derived for one representative body position.

FIG. 11 illustrates an exemplary composition of the health indicatorvector shown in the data workflow of FIG. 5.

FIG. 12 illustrates exemplary data processing for obtaining posturaldeviations from 3D body topography data.

FIG. 13 illustrates an exemplary data processing workflow for obtainingvarious predictions of health conditions and risks based on the healthindicator vector of FIGS. 5 and 11 and other auxiliary data inaccordance with the distributed data processing system of FIGS. 1 and 2.

FIG. 14 illustrates an exemplary automatic and intelligent PHCI dataworkflow in accordance with the distributed data processing system ofFIGS. 1, 2 and 13.

FIG. 15 shows an exemplary PHCI matrix and patient health conditionvector in the context of postural abnormality conditions using exemplarypostural deviation vector components.

FIG. 16 shows exemplary health indicator vectors and their quantizationusing a quantization table.

FIG. 17 shows an exemplary data workflow for intelligent PPRHA includinga PPRHA model and engine in accordance with the distributed dataprocessing system of FIGS. 1 and 2.

FIG. 18 shows an exemplary post-advocacy data flow diagram withautomatic monitoring of patient implementation of PPRHA items.

FIG. 19 illustrates various optional weighted feedback paths and dataworkflow into the intelligent PPRHA engine of FIGS. 1, 2, and 17.

FIGS. 20-26 illustrate exemplary graphical user interfaces on aphysician terminal device for accomplishing physician functions in thesystem of FIG. 1.

FIGS. 27-31 illustrate exemplary graphical user interfaces on a patientterminal device for accomplishing patient tracking, monitoring, andother functions in the system of FIG. 1.

FIG. 32 shows an exemplary hardware composition of various processingengines, servers, and terminal devices of the distributed dataprocessing system of FIG. 1.

FIG. 33 illustrates an exemplary data workflow for the data collectionmanager of FIG. 2.

FIG. 34 illustrates an exemplary embodiment of a deep learning model.

FIG. 35 shows an exemplary logic flow for automatic and intelligent PHCIand PPRHA.

DETAILED DESCRIPTION

Health screening, health risk assessment, disease intervention, patienthealth condition identification (PHCI), and patient predictive/remedialhealth advocacy (PPRHA) traditionally rely on manual examination, byphysicians, utilizing PHD for an individual patient collected atphysically centralized medical facilities such as hospitals, medicalcenters, clinics, and medical/biological labs. PHD may include but arenot limited to images (e.g., X-ray images, CT images, MRIs, ultrasoundimages, mammograms, and vascular images), electrocardiograms,anthropometrics (e.g., weight, height, and 3D body topography),respiratory rate, heart rate, body temperature, and systolic anddiastolic blood pressures.

While generation of some PHD, such as CT images and MRIs, currentlyrequire complex and expensive equipment that is centrally hosted,generation and collection of other PHD may only require non-invasivetechnologies that are much more accessible by the general publicregardless of location. Portable, affordable, and/or patient-operatedhealth measurement and monitoring devices are now available and can beconveniently distributed in homes, self-service kiosks, as portablebiological lab kits, or even as wearables. In the meanwhile, (1)centralized or distributed computing devices/components withcapabilities based on machine-learning technologies such as computervision and other types of artificial intelligence, and (2) computersecurity technologies for guarding patient and physician indentities anddata in a global networked environment have emerged.

Distributed health measurement and monitoring devices further enablemore frequent or continuous and near-real-time measurements andmonitoring of a patient, and may provide comprehensive time-sequenceinformation that may not be available at centralized medical facilitieswhere patient visits are less frequent. Distributed devices may bediverse in form and may generate additional new data. Additional newdata may be associated with patient health conditions and thus mayprovide valuable additional information in helping improve accuracy inhealth risk assessment, screening, PHCI, and PPRHA.

While the additional new data can theoretically be useful foridentifying patient health conditions by physicians, the number ofimages, the measurements, and image patterns in the data may be toodetailed and volumetric to be considered as key factors indicating ahealth condition by physicians during a patient visit. However, theassociations between the measurements and image patterns and healthconditions and remedies, may be automatically learned, identified andextracted by computer models trained using complex machine learningalgorithms and architectures such as multilayer neural networks.Computer models may be implemented in backend servers speciallyconfigured to provide massive parallel data analytics and computationalcapability for artificial intelligence applications. The patients andthe physicians may electronically communicate with backend servers viatheir terminal and/or mobile devices to form an integral data workflowfor data collection, analytics, health risk assessment, PHCI, PPRHA, andmonitoring.

This disclosure describes such an automatic, distributed,computer-aided, and intelligent system and platform for healthmonitoring and data collection/analytics. The system integrates (1)distributed PHD collection servers and devices, (2) centralized ordistributed data servers running various intelligent and predictive dataanalytics engines for health screening, risk assessment, PHCI and PPRHA,(3) specifically designed data structures including quantized healthindicator vectors, a quantized PHCI matrix, and patient health conditionvectors, (4) portal servers configured to interface with (5) distributedphysician terminal devices, and (6) distributed patient terminal devicesfor receiving health evaluations, delivering interventions and PPRHAitems, and for monitoring and tracking patient activities. The variousintelligent and predictive engines are configured to recognize, extractand analyze patterns in data from large heterogeneous data setscollected from distributed data collection devices. The architecture ofthe system and platform disclosed herein further uses a mixed supervisedand unsupervised machine learning approach to train various artificialintelligence models with weighted multi-path feedback for retraining themodel and improving intelligence of the models (e.g., a PPRHA engine).The system disclosed herein is based on computer technologies anddesigned to use artificial intelligence tools to solve technicalproblems in computer-aided health screening, risk assessment, PHCI, andPPRHA.

The intelligent system disclosed herein may be further capable ofpredicting various types of health conditions and risks for a patient byidentifying patterns indicating a health issue from collected data, inaddition to particular PHCI and PPRHA items. Risks include, but are notlimited to, cardio-pulmonary risk, neurological risk, diabetic risk,intestinal permeability risk, intervertebral disc degeneration risk, andother postural risks. Accurate and timely prediction of health risksallows provisioning of preventive measures that may significantly reducehealth costs, prevent further illness, and even prevent/delay death.While some of the risk assessment may be traditionally provided inregular medical exams and health screenings, some risks may beunconventional and are more difficult, if not impossible, to assessmanually by physicians. For example, risk of falling due to posturalimbalance is difficult to assess manually. Yet, a large percentage ofdeaths in the elderly population are attributed to falling, orindirectly due to complications caused by falling, including hipfractures, cervical fractures, and death due to the decline of postureand balance control. By using the computer-aided system and methodologydisclosed below, risk of falling may be predicted intelligently andaccurately, using for example, computer-aided analytics of 3D bodytopography data in the form of 3D body mesh scan and other auxiliarydata.

This system thus includes data collection, data analysis, and datastorage components that provide more accurate and complete informationto assist medical professionals to provide further PHCI, identify healthimprovement opportunities, and implement interventions to achievedesired outcomes. In different embodiments, this system may be suitablefor providing computer-aided rehabilitation, therapeutic PHCI and PPRHAfor reducing functional impairments and health complications.

The intelligent system disclosed herein may in particular facilitateunmet health care needs in rural areas by expanding access to care.Health care provisioning in rural areas face various barriers, includingbut not limited to transportation difficulties, limited supplies, lackof health care quality, lack of health care professionals, socialisolation, and financial constraints. Rural residents have highermorbidity and mortality rates compared to their urban counterparts.Distributed PHD collection and remote computer-aided screening, PHCI,and PPRHA described herein may serve as a platform to bypass rural areahealth care barriers and provide more accessible and effective healthservices.

The term “PPRHA” is used in this disclosure to broadly refer to anyindividual or combined preventive or remedial advocacy items prescribedfor a particular one or more health conditions. PPRHA items may includebut are not limited to prescriptive or nonprescriptive medicine,supplements, self-directed management, at-home care, therapies,medical/biological tests, and referral of medical/clinical facilities orphysicians. The term “PPRHA” may be alternatively referred to as patienttherapeutic interventions. Likewise, the term “therapeutic” is usedherein to broadly refer to prescriptive or nonprescriptive medicine,supplements, self-directed management, at-home care, therapies,medical/biological tests, referrals, and the like based on the patienthealth conditions.

FIG. 1 shows an example of such a distributed, automatic, andintelligent health monitoring and data analytics system 100. In theembodiment of FIG. 1, system 100 includes distributed PHD collectiondevices 104 and 106, physician terminal devices 118 operated byphysician 120, patient tracking terminal devices 122 operated bypatients 124, data repository 112, various data analytics enginesincluding an intelligent PHCI engine 114 and an intelligent PPRHA engine108, and portal servers 102 for providing the physician terminal devices118 and patient tracking terminal devices 122 access to the dataanalytics engines (108 and 114) and data, and for enabling collaborationbetween the patients 124 and the physicians 120.

As shown in FIG. 1, each component of the system 100 may be locatedanywhere in the world and some system components, such as the datarepository 112 and each of the data analytics engines 108 and 114 andportal servers 102 may further be distributed over multiple geographicallocations. Some components of the system 100 may be virtualized and maybe implemented in a computer cloud. While only a single instance isillustrated in FIG. 1 for some components of the system 100, the numberof instances in an actual implementation is not limited. For example,there may be multiple physician participants 120 and multiple patientparticipants 124, each being provided with a separate account in theportal servers 102. In addition, each patient 124 or physician 120 mayaccess its account in the portal servers 102 via any number of terminaldevices 122 and 118. The physician terminal devices 118 and the patienttracking terminal devices 122 may be fixed or mobile, implemented informs including but not limited to desktop computers, laptop computers,personal digital assistants, augmented reality devices, mobile phones,and mobile tablets. All components of the system 100 above may beinterconnected by communication networks 110. The communication networks110 may include private and/or public wireless or wireline networksdefined by, for example, any known network protocols and/or stacks. Thecommunication networks 110 may implement any security technologies thatare currently known or developed in the future for protecting patientand physician privacy during data access and transmission. For example,end-to-end encryption and security protocols that satisfy or exceed,e.g., HIPAA requirements, may be implemented.

The distributed data collection devices 104 and 106 may be installedwith various types of sensors. The distributed data collection devices104 and 106 may be distributed in homes or in a network of self-servicekiosks. The distributed data collection devices 104 and 106 may belocated in medical centers, hospitals, clinics, and centralized labs. Insome embodiments, the distributed data collection devices 104 and 106may be distributed as wearables, such as watches and bracelets. In otherembodiments, the distributed data collection devices 104 and 106 may beconfigured as an autonomous service kiosk. In some further embodiments,the distributed data collection devices 104 and 106 may be part of andmay be integrated with the patient tracking terminal devices 122.

In one particular embodiment, as will be described in more detail belowin relation to FIGS. 3 and 4, the distributed data collection devices104 or 106 may include one or more 3D body scanners. Each of the 3D bodyscanners 104 or 106 may be capable of generating 3D topography data of apatient body using any suitable scanning technology in the form of 3Dbody mesh scan. Like other health data, the 3D body topography data mayinclude information that is associated with various health conditionsand may be used to facilitate general or specific health screening,health risk assessment, PHCI, and PPRHA. For some particular healthconditions, e.g., kyphotic conditions related to postural abnormality,the 3D topography data may constitute a major source of information forPHCI of a particular abnormal postural condition and for correspondingtherapeutic PPRHA items (e.g., physical therapy treatments).

FIG. 2 shows an exemplary data workflow system 200 for system 100 ofFIG. 1. In FIG. 2, rectangular boxes are used to denote data processingwhile rounded boxes are used to denote data structures. The arrowsindicate directions of data flow. The labels on the arrows indicate mainsystem components of FIG. 1 involved in corresponding data flow. Theauxiliary labels in brackets following some of the main labels forvarious boxes likewise denote the system components in FIG. 1 involvedin corresponding processes or used for storing corresponding data. WhileFIG. 2 uses 3D topographical body scan data as an illustrative example,the underlying principles for data processing, modeling, and analyticsin FIG. 2 are broadly applicable to any other type of PHD generated bythe distributed data collection devices 104 and 106 of FIG. 1. In FIG.2, the 3D topography input data may be collected via a 3D scanningprocess 202 using the distributed 3D body scanner 104 or 106. The 3Dtopography data 204 may be collected for a particular person for one ormore of various predetermined sets of body positions including but notlimited to standing, bending, and squatting.

Continuing with FIG. 2, the 3D topography data 204 may be converted in adata conversion process 206 by the intelligent PHCI engine 114 of FIG. 1to generate a health indicator vector 210 having a vector spaceincluding one or more vector components each being an indicator of oneaspect of patient health. The health indicator vector 210 may be used bythe intelligent predictive PHCI engine 208 (114) in an intelligent PHCIprocess to generate a patient health condition (PHC) vector 209corresponding to a PHC vector space including dimensions representingvarious predetermined health condition (or diseases). Optionally, thefull original and unprocessed 3D topography data 204 may also be used asanother input to the intelligent PHCI process performed by theintelligent PHCI engine 208 for the generation of the PHC vector 209.

The data workflow 200 may further include a data collection manager 211which aggregates various data including but not limited to the 3Dtopography data 204, the PHC vector 209, other patient data 232(including e.g., patient survey data, patient registration data, andother patient data), and data indicated by arrows 240 (discussed in moredetail below). The data collection manager 211 may process data, as willbe described in more detail below with respect to FIGS. 17 and 33 andgenerate input data to the intelligent PPRHA model or engine 212.

The output of the data collection manager 211 may then be input to theintelligent PPRHA model 212 to generate the PPRHA items 216. Theautomatically generated PPRHA items 216 may be transmitted to thephysician terminal device 118 via the portal server 102 and anotification process 218. The PPRHA items 216 may be presented to thephysician in a graphical user interface on the physician terminal device118. The physician may be allowed to review (at 219) the 3D topographydata 204 and health indicators of the patient (not shown as an input toprocess 219 in FIG. 2 for simplicity) and the PPRHA items 216, andprovide modification to the PPRHA items 216 (if needed) in a graphicaluser interface on the physician terminal device 118 to generate modifiedPPRHA items 220. The modified PPRHA items 220 are then transmitted tothe patient tracking terminal device 122 via the portal server 102 and apatient notification process 221. The modified PPRHA items 220 may bepresented to the patient in a graphical user interface on the patienttracking terminal device 122. The patient may then implement themodified PPRHA items 220 and the implementation may be monitored by thepatient tracking terminal device 122 as shown by process 222. Thepatient implementation, for example, may be monitored and recorded aslogs and stored in the data repository 112.

Patient implementation of the modified PPRHA items 220 may be augmentedby educational repository materials 224 extracted from the datarepository 112. The educational materials may be selected from aneducational library in the data repository 112 based on the modifiedPPRHA items 220 and the patient-selected educational materials 224 maybe presented to the patient via the graphical user interface on thepatient tracking terminal device 122 via patient monitoring process 122.Educational materials may include but are not limited to demo videos,text, graphical, simulative, and/or pictorial descriptions forexplaining and demonstrating how the modified PPRHA items 220 (e.g.,physical therapy exercises) should be implemented.

In some embodiments, the patient may be provided with self-evaluationtools via the graphical user interface on the patient tracking terminaldevice 122, as shown by process 226. The patient may further berescanned after implementing a predetermined amount of the modifiedPPRHA items 220 and/or for a predetermined period of time, or at anytime, as shown by 3D rescanning process 228. The rescanned 3D topographydata 230 may be recorded in the data repository 112 along with the timeof rescan. The rescanned 3D topography data 230 of the patient mayfurther be processed in a manner similar to the original 3D topographydata 204 to generate a new health indicator vector. The new healthindicator vector may be stored in the data repository 112. As such, thedata repository 112 may include a time sequence of health indicatorvectors recorded for each patient.

In FIG. 2, the intelligent PPRHA model may optionally be improved bytaking into consideration several feedback paths and feedback inputsincluding but not limited to the physician review and modification ofthe PPRHA items 219, the monitored log 222 of patient implementation ofthe modified PPRHA items 220, the patient self-evaluation process 226,and the rescanned 3D topography data 230 and the corresponding healthindicator vector (which can be evaluated at different times to form atime sequence). Input feedback to the data collection manager 211 asshown by arrows 240 may be used by the intelligent PPRHA model asadditional data for generating the PPRHA items 216 via the intelligentPPRHA model 212, and for facilitating the data collection manager 211 toperform its function in generating an original and updated trainingdataset for training and retraining the intelligent PPRHA model 212, asdescribed in more detail below with respect to FIG. 17 and FIG. 33.

FIG. 3 illustrates an example of the distributed data collection devices104 and 106 in the form of a 3D body scanner 300. The 3D body scanner300 may include one or more sensors 302/304 and a force plate 306.Sensors 302 and 304 may be implemented as digital cameras or may bebased on other optical sensing technologies. Alternatively, sensors 302and 304 may be implemented as laser scanners using class I laser beamsin the infrared optical spectral range. The 3D topography data may beobtained based on, for example, laser ranging and time-of-flighttechnologies. For another example, sensors 302 and 304 may be based onstructured light and optical detection technologies associated withstructured light.

The force plate 306 may be configured as a platform for a patient tostand. The force plate 306 may be segmented into independent sensinggrids such that both the magnitude and distribution of force or pressureexerted on the force plate 306 may be detected. Sensing grids maydetermine the static pressure distribution due to patient posturalimbalance and dynamic patient functional movement characteristics.Patient postural sway may be captured on the force plate 306 in a singlepostural snapshot or in a series of continuous or discrete posturalsnapshots in time. As such, the force plate 306, in addition to thesensors 302 and 304, may provide one or more segmented (or pixelated)pressure or force data that may be used to help determine, for example,the static balancing attributes and the dynamic balancing attributes (orfunctional movement or sway attributes) for the patient. The force datamay additionally be used to establish a reference frame for the 3D bodytopography data for various predetermined body positions, as describedfurther below.

The sensors 302/304 and the force plate 306 may be configured withmotion capability for the collection of body topography data in 3D. Forexample, sensors 302/304 may be mounted on translation stages such thatthey may be controlled to move vertically (shown by 310 and 312) orhorizontally (not shown in FIG. 3). The force plate 306, for example,may be installed on a rotary stage such that the force plate 306 may beconfigured to rotate around a predetermined axis, as illustrated by 314.Alternatively, rather than rotating the force plate 306, the sensors302/304 may be installed on a cylindrical frame configured to rotatearound its central axis. The linear motion or rotation of the sensors302/304 or the force plate may facilitate the collection of the 3Dtopography data of a patient.

The sensors 302 and 304 may be configured to obtain raw topography dataof a patient located on the force plate 306. In some embodiments, thesensors 302 and 304 may be digital cameras and the 3D body scanner 300may be configured to analyze photographs taken (or images captured) bythe sensors 302 and 304 from different positions or angles, andthereafter to extract topographical information using digital objectdetection and recognition technologies based on multilayer convolutionalneural network models. In some other alternative embodiments, sensors302 and 304 may comprise laser scanners based on optical rangingtechnology for obtaining body topographical information of the patient.

The components of the 3D body scanner 300 may be configured to obtain aset of topography data and force data for a patient in differentpostural positions including, but not limited to, standing, bending, andsquatting. For each position, the sensors 302/304 and the force plate306 and their translational/rotational mechanisms may be configured tocollect and capture a single snapshot of the patient. The sensors mayfurther be configured to take a series of snapshots of the patient. The3D body scanner 300 may further include a display screen 316 fordisplaying menus and showing images or videos demonstrating the variouspostural positions for the patient when capturing topographicalsnapshots.

While the description above provides some examples of image sensors 302,304, and force plate (sensor) 306, other types of sensors may be furtherincluded in the 3D body scanner 300 to provide data that can facilitateintelligent and accurate PHCI and PPRHA by the engines/models 208 and212 of FIG. 2. For example, the 3D body scanner 300 may include camerasthat are capable of capturing facial and eye images. As will bedescribed below, facial and eye images may be associated with orindicative of stressors and may further be associated with the posturalcharacteristics of the patient. Such data may be provided to the datacollection manager 211 of FIG. 2 to facilitate intelligent PHCI andPPRHA. As another example, thermography sensors may be included with the3D body scanner 300. Such thermography sensors may be configured toprovide detection of temperature distribution of a target patient inaddition to topographical data. The temperature distribution informationmay be indicative of inflammation, increased metabolic activity, andother conditions and may be used in conjunction with the topographicaldata to facilitate intelligent PHCI and PPRHA by the engines/models 208and 212 of FIG. 2. Now conversely related to increased heat detection,thermography can detect areas of the body that have decreasedtemperature that is indicative of decreased blood flow, poorcirculation, and other hypovascular disorders. Detecting hypovasculardisorders in relation to poor body alignment and posture can givefurther insights to clinicians regarding the physical health of theirpatients. For yet another example, sensors based on radio waves, such aselectromagnetic waves in the millimeter wavelength range, may beincluded with the 3D body scanner 300. In some embodiments, radio wavesensors may be used to automatically detect pressure changes in a humanbody without physical contact. The pressure changes may be related tobreathing, blood flow, and swelling. For example, a radio wave sensormay detect decreased blood flow in the feet of individuals with poorbalance. Such detection will give valuable information relating toabnormal balance conditions. Adding a radio wave sensor thus may furthersupplement the accuracy of PHCI when used in conjunction with 3Dscanning and a force plate sensor. Lastly, red light spectroscopyprovides another sensing technique that may be used in conjunction with3D body scanner 300. Red light spectroscopy can detect the oxygenationin blood flow within a human body.

The electronic components of the body scanner 300 of FIG. 3, are shownin FIG. 4. The sensors and force plate 402 produce digital signals whichmay be further processed by the processing circuits 404 to produce rawtopography data 406 and raw force data 408. The raw topography data 406and raw force data 408 may then be communicated to the other componentsof the system 100 of FIG. 1, via communication interface 410.

The raw 3D topography data 406 and force data 408 may be furtherprocessed by the intelligent predictive PHCI engine 114 of FIG. 1 togenerate a health indicator vector 210 of FIG. 2. An exemplary data workflow for data processing and analytics is shown in FIG. 5. In thisembodiment, the raw topography data and force data 501 may be analyzedby the intelligent predictive PHCI engine 114 using various dataanalysis processes 502 to generate intermediate data, which are furtherprocessed to produce the health indicator vector 540. The healthindicator vector 540 may be further quantized using a quantizationprocess 542 to generate a quantized health indicator vector 546.Exemplary components of the health indicator vector 540 and quantizedhealth indicator vector 546 will be disclosed in more detail withrespect to FIG. 11 below. The quantization 542 of the health indicatorvector 540, may facilitate setting data range limits, simplifying andspeeding up data processing following the generation of the quantizedhealth indicator vector 546 in the rest of the PHCI process performed bythe intelligent predictive PHCI engine 114 of FIG. 1 and the PPRHAprocesses performed by the intelligent PPRHA model 212 of FIG. 2. Anexample for quantization 542 of the health indicator vector 540 will begiven in more detail below with respect to FIGS. 15 and 16.

In some embodiments, the data analysis processes 502 may include variousexemplary processing components. For example, the data analysisprocesses 502 may include but are not limited to (1) identifying avertical reference line and a set of reference planes for normalizingthe raw 3D topography data based on the raw 3D topography data andoptionally the force data (504); (2) identifying a predetermined set ofbody landmarks (alternatively referred to as body segments) from thenormalized 3D topography data (506); (3) identifying single-point ormulti-point representations of body landmarks based on the normalized 3Dtopography data (508); (4) identifying static and dynamic balancecharacteristics of the patient based on single body/force plate snapshotor data set of snapshots (510); (5) identifying various circumferences,their ratios, and their centers of mass from the normalized 3Dtopography data (512); (6) identifying body-mass-index (BMI), a bodyshape index (ABSI), body fat index or percentage, and trunk-to-legvolume based on the normalized 3D topography data (514); (7) determiningbody alignment score and effective spinal age based on the normalized 3Dtopography data (516); and (8) determining an intervertebral disc (IVD)score (520).

FIG. 6 shows an exemplary illustrations for the process 504 ofidentifying the vertical reference line and the set of reference planesbased on the raw 3D topography data and/or the force data. The verticalreference line and the set of reference planes may be used to furthernormalize the raw 3D topography data into orientation normalized forstandardized 3D body topography data. The establishment of the referenceframe is described in FIG. 6 and below for two exemplary posturalpositions: standing position (600) and squatting position (601). Theunderlying principles described here are similar between these exemplarypostural positions 600 and 601. The description below for FIG. 6 isbased on the standing position (600) but applicable to other posturalpositions unless otherwise explicitly stated. Corresponding descriptionfor data representations in FIGS. 7-10 are also exemplarily given forstanding position but applies to other postural positions.

Continuing with FIG. 6 for the standing position 600, and in oneembodiment, the heels of the patient may be recognized using computervision and object recognition models. The center point 602 between theheels may further be determined and may be used to define the verticalreference line 616 of the patient. In some other embodiments, forcedistribution on the force plate 306, as represented by the force data408 of FIG. 4, may alternatively be used by process 504 to determine thecenter point 602 between the heels of the patient. Again, the centerpoint 602 may then be used to determine the vertical reference line 616of the patient. The vertical reference line 616, for example, may bedetermined as a line that originates from the center point 602 of theheels and extends in the vertical direction. The vertical reference line616 in conjunction with the raw 3D body topography data 406 may be usedto determine a sagittal plane 618 and a coronal plane 622. The sagittalplane 618, for example, represents a plane intersecting the patient intoa left half and a right half. The coronal plane 622, on the other hand,represents a plane perpendicular to the sagittal plane 618 andintersecting the patient into a front half and a back half. The sagittalplane 618 and the coronal planes 622 intersect at the vertical referenceline 616. The identification of the sagittal plane 618, for example, maybe based on recognition, by computer vision function in process 504, afront-facing direction of the patient according to the raw 3D topographydata 406. Such a direction would be normal or perpendicular to thesagittal plane 618. A transverse plane 620 parallel to the ground may befurther determined from the raw 3D original topography data 406. Thetransverse plane 620, for example may encompass the center point 602 ofthe heels of the patient.

The sagittal plane 618, the coronal plane 622, and the transverse plane620 further form a body reference frame 630 for the patient in FIG. 6.The original raw 3D body topography data 406 may then be normalizedaccording to the body reference frame 630 to generate the normalized orstandard 3D body topography data set. The normalized body topographydata, for example may be described using Cartesian coordinates having anorigin at the center 602 of heels, and planes 618, 622, and 620 as theCartesian reference planes.

FIG. 7 shows different exemplary views 700 of the normalized 3D bodytopography data set for the standing position, including side-view 702,front-view 704, and top-view 706. Other views from any predefined angel,e.g., view 708, may also be generated. The views 702, 704, and 706, and708 may be generated by projecting the normalized 3D body topographydata into corresponding projection planes.

The left panel 800 of FIG. 8 illustrates identification of apredetermined set of body landmarks in accordance with process 506 ofFIG. 5 and identification of single-point or multi-point representationof body landmarks in accordance with the process 508 of FIG. 5. The setof body landmarks for example, may include but are not limited to head,803, shoulders (including right shoulder 805 and left shoulder 807),hips (including right hip 809 and left hip 811), knees (including rightknee 813 and right knew 815), and ankles (including right ankle 817 andleft ankle 819). In some embodiments, models may output a single-pointor multi-point representation for each of the body landmarks in the setof body landmarks. Computer models may be used to recognize from thenormalized 3D body topography data portions of the data associated witheach of the predetermined body landmarks. Computer models may furtheroutput a single-point or multi-point representation for each of bodylandmarks. For example, single-point representation may be determinedfor the head, the right shoulder, the left shoulder, the right hip, theleft hip, the right knew, the left knew, the right ankle, and the leftankle, as shown by 802, 806, 808, 810, 812, 814, 816, 818, and 820,respectively.

The single-point or multi-point representation above may be derived inthe form of Cartesian coordinates in the body reference frame 630discussed above with respect to FIG. 6. The representation points ofbody landmarks may be internal to the body surface as represented by thebody topography. Likewise, the models above may further outputrepresentations of internal body landmarks that are not part of thetopography. For example, a multi-point representation 804 forming thespinal line may be determined from the normalized 3D body topographydata.

Continuing with FIG. 8, the middle panel 801 illustrates determinationof patient static balancing characteristics based on the 3D bodytopography data and/or the force data using process 510 of FIG. 5. Thestatic balancing characteristics may be used for describing posturalimbalance that could pose falling risk. The characteristics are staticin that they represent postural imbalance due to static bodymisalignment. Characteristics may be determined by a single topographicsnapshot as shown in 801 and/or corresponding force data. Balancingcharacteristics may be derived, for example, by analyzing variousalignment lines 832, 834, 836, and 838 in conjunction with the bodyweight distribution along the alignment lines derived from the 3D bodytopography data in relation to the reference vertical line 830 (616 ofFIG. 6). The static balancing characteristics may be further quantifiedto represent a falling risk for the patient. In other embodiments, thestatic balancing characteristics may be derived from force data. Inparticular, the force distribution on the force plate may be analyzed todetermine whether the patient is unbalanced in posture. For example,force data showing that the patient weight is more distributed on oneheel than the other heel may be an indication that the patient isunbalanced left to right. For another example, force data showing thatthe patient weight distribution ratio between toes and heals is higherthan normal may be an indication that the patient is unbalanced frontand back. In yet some other embodiments, the 3D body topography data andforce data above may be used in combination to determine the staticbalancing characteristics for the patient.

The static balancing characteristics above may be obtained from a singlesnapshot of the 3D topography data and/or force data. Multiple snapshotsmay be acquired from the patient by the body scanner and analyzed todetermine the dynamic balancing characteristics or functional movementcharacteristics of the patient. Characteristics are dynamic since thesnapshots may be taken at different times and used to determine postureinstability. Snapshots may be taken as a time sequence. In someembodiments, the snapshots may be taken periodically and/or continuouslyduring a predetermined amount of time. In other embodiments, snapshotsmay be taken at random times and analyzed statistically to determine theinstability of patient posture. Instability in posture may be identifiedin the form of patient body sway. Patient body sway is shown in theright panel 803 of FIG. 8. Patient body sway may be determined, forexample, by detecting deviation of the vertical center body line 840 ofthe patient from the vertical reference line 830 as a function of timeor as a statistical distribution. The vertical center body line 840, forexample, may be a line connecting the center of heels and center betweenthe right and left shoulders. Other lines may also be used to representthe vertical center body line 840. The vertical center body line 840 maysway backward, forward, left, and/or right as a function of time, asshown by 830 and 840. The balancing characteristics may be representedby an amount of spread and direction of spread of the vertical centerbody line 840.

In other embodiments and analogous to the static balancingcharacteristics above, the dynamic balancing characteristics, e.g., bodysway, may alternatively be determined based on a sequence of force datashowing variations of force distribution over time for the patient. Forexample, the pressure distribution detected by the force plate betweentwo feet, between heel and toe of each foot, and within each heel oreach toe may vary in time as the patient sways backward, forward, left,or right. The spread of such pressure distribution may be captured bysnapshots of force data and used to determine the patient dynamicbalancing characteristics, which may include but are not limited to theamount of pressure deviation and direction of deviation.

Like the static balancing characteristics, the dynamic balancingcharacteristics (or functional movement characteristics) may bequantified and used for representing risk of falling for patient. Thedynamic balancing characteristics and static balancing characteristicsmay be separately analyzed to represent a static risk of falling and adynamic risk of falling. In some alternative embodiments, the static anddynamic balancing characteristics may be combined to derive an overallrisk of falling for the patient.

Turning back to FIG. 5 and referring to process 512, FIGS. 9-10illustrate determination of various body circumferences andcircumferential ratios from the normalized 3D body topography data.Ratio between circumferences at different parts of the body may alone orin combination with other parameters provide indication of patienthealth in various aspects. Circumferences of chest, waist, hips, forexample, may be derived from the 3D topography data. The circumferencesat different parts of the body may be derived from the scannedtopography data with much improved accuracy compared to traditionalphysical measurements using a tape ruler. For example, traditionalmeasurements of waist circumference using physical measuring tapes maynot account for various skin folding and thus may lack measurementaccuracy.

FIG. 9, for example, depicts body circumferences 900 in side-view 902,front-view 904, top-view 910 and view 908 along another predeterminedangle. Portions 920 of a particular circumference illustrates a skin orsurface folding in the patient body that may not be accurately measuredusing a tape ruler. As shown in FIG. 9, circumferences for head,shoulders, body trunk, and hips may be single circumferences at eachvertical position. Circumferences for the arms and legs may include leftand right circumferences at each vertical position.

FIG. 10 shows the circumferences 1000 in more detail. For example,particular circumferences, such as 1004, are shown as closed curves. Thespreadsheet 1002 further shows the exemplary circumferential coordinatesin the body reference frame 630 of FIG. 6 for two particularcircumferences (referred to as shapes in 1002 of FIG. 10). FIG. 10further illustrates determination of center of mass coordinate data foreach of the circumferences at various vertical positions, shown aspoints 1006 in FIG. 10. The centers of mass may be determined byanalyzing the shape of each of the circumferences 1004. The centers ofmass may form lines as shown by the dots 1006 in 1000 of FIG. 10. Thelines, for example, may be further used to determine alignment lines,e.g., lines 832, 834, 836, and 838 of FIG. 8. The alignment lines, asdiscussed above with respect to FIG. 8, may be used to determine thestatic and dynamic balancing characteristics of the patient alone or incombination with the force data.

Referring back to FIG. 5, other process such as 514, 516, and 520 indetermining various other body parameters, such as BMI, ABSI, body fatpercentage, truck to leg volume, body alignment score or quantification,effective spinal age, and IVD score may be further invoked. Theprocesses 514, 516, and 520 are not further shown in additionaldrawings, but a person with ordinary skill in the art understands theparameters may be derived from normalized 3D body topography data and/orforce data.

FIG. 11 illustrates an example of the health indicator vector 540 ofFIG. 5. The health indicator vector may include multiple components in amulti-dimensional health indicator vector space. The components may beused as direct or indirect indication of patient health in variousaspects. The components may include but are not limited to posturalcomponents 1104, effective spinal age component 1114, body shapecomponents 1112, static balance components 1118, dynamic balancecomponents 1120, and body composition components 1121. The posturalcomponents 1104 may further include but are not limited to front-viewpostural deviations 1106, side-view postural deviations 1108, top-viewpostural deviations 1110, and postural position discrepancy 1111.Postural deviation of each of the predetermined set of body landmarksmay be represented by one or more components of the health indicatorvector 540. A deviation may be a shift, a tilt, or a rotation of thecorresponding body landmark from normal reference values (e.g., zeroshift, zero tilt, or zero rotation), as will be disclosed in furtherdetail below with respect to FIG. 12. Postural deviations in differentviews may be separately represented by deviation components for each ofthe predefined postural positions described above with respect to FIG. 6(including but not limited to standing position, bending position, andsquatting position). For some patients, severity of postural deviationmay differ between different postural positions. For example, a patientmay have little postural deviation at standing position but may havesignificant deviation in other postural positions. Variations ofpositional postural deviation may be associated with certain posturalproblems that can be diagnosed and remedied. In some embodiments, apostural positional discrepancy component 1111 may be included as partof the postural components 1104 of the health indicator vector 540. Thebody shape components 1112, for example, may further include but are notlimited to BMI 1122, ABSI 1124, circumferences and circumferential ratio1113, body volume 1116, and body alignment 1117 components, as discussedabove with respect to FIG. 5. The body composition components 1121, forexample, may include body fat percentage component 1126 and bone density1128, which may, for example, be derived from the 3D body scan data.

The various components of the health indicator vector 540 above aremerely provided as examples. Other types of components may also beincluded in the health indicator vector 540. For example, as describedabove with respect to FIG. 6, the 3D body scanner 300 may includesensors that capture facial or eye images of the patient. Facial and eyeimages may be analyzed to derive features that are correlated withstressors and other health conditions. The facial and eye features maybe associated with postural issues. Identified facial and eye features,when included as one or more components of the health indicator vector540, may be used by the PHCI engine/model (208 of FIG. 2 above, and 1404of FIG. 14 below) to associate the data with various health conditionsto provide more accurate health condition indication.

FIG. 12 illustrates exemplary embodiments of determining posturaldeviations of body landmarks (postural components 1104 of FIG. 11) fromthe 3D body topography data. Postural deviations, for example, maycomprise postural shifts, postural tilts, and postural rotationsdeviating from natural values of body landmarks. Deviations may beascertained from front-view, side-view, and/or top-view. Postural shiftsmay be determined as deviation of body landmarks away from the verticalreference line. Postural tilts may be determined as deviation oftransverse planes of body landmarks away from the horizontal groundplane. For a body landmark (e.g., shoulder or hip) having left and rightparts, corresponding postural tilt may be determined by an angle formedbetween the ground plane and a line connecting the representation pointof the left part and the representation point of the right part of thebody landmark. Postural rotation deviation, on the other hand, may beascertained from the top-view. Postural rotation deviation, for example,may be used to represent abnormal rotation of a body landmark in thehorizontal plane around vertical reference axis line.

The left panel 1200 of FIG. 12 illustrates one embodiment forcalculating postural tilt and shift deviations. A left representationpoint 1204 and right representation point 1206 of a body landmark may bedetermined as disclosed above with respect to FIG. 5. Line 1214connecting the left representation point 1204 and the rightrepresentation point 1206 may be determined. Center point 1208 betweenthe left and right representation points may be identified and itscoordinates with respect to the reference frame of FIG. 6 may bedetermined. The deviation of the center point 1208 from the verticalreference line 1202, as shown by 1210, may be identified as the shiftdeviation. Furthermore, difference between the left and rightrepresentation points 1204 and 1206 along the vertical reference line1202, as shown by 1212 may be identified. In one embodiment, a ratiobetween this difference 1212 and the horizontal distance between theleft and right representation points (as shown by 1216) may bedetermined as the tilt deviation. The process for identifying shiftdeviation and tilt deviation may be applied to body landmarks such asshoulders, hips, and knees.

The right panel 1201 of FIG. 12 illustrates one embodiment fordetermining postural rotation deviation in top-view. For example, normalbody landmark position 1224 in top-view may be represented by axis 1220.The actual position of the body landmark 1226 and its axis 1222 in topview may be identified, again, based on computer vision and objectrecognition models. A deviation in orientation between the normal axis1220 (pointing to normal front and back) and the actual axis 1222 may beidentified to represent the rotation deviation, as shown by 1230.

The various deviations above, may be sign sensitive, i.e., positivedeviation may represent deviation in one direction and negativedeviation may represent deviation in an opposite direction. Any otherpredetermined derivatives of the deviations described above (distances,ratios, or angles) rather than the deviations themselves mayalternatively be used to represent the shifts, tilts, and rotations. Thederivatives, in turn, may be used as the various postural deviationcomponents 1104 in the health indicator vector 504 of FIG. 11.

FIG. 13 shows another data work flow 1300 for generation of varioushealth scores for health-screening and assessment using the quantizedhealth indicator vector 546 of FIG. 5 and other auxiliary data. Thistype of data work flow may be part of the data collection manager ofFIG. 2. Auxiliary data, for example, may include but are not limited topatient data 232 of FIG. 2 such as patient registration data and surveydata maintained by the portal server 102 of FIG. 1. In one embodiment,the quantized health indicator vector 546 may be used to derive posturalPHCI 1306 (as discussed above in FIG. 2 as generated by the PHCIengine), postural and body alignment score 1308, IVD degeneration riskprediction 1310, effective spinal age prediction, 1312,cardio-pulmonary, neurological, diabetic, and intestinal permeabilityrisk prediction 1314. Some postural PHCI, scoring, and risk predictionprocesses may be augmented by the patient data 232 including, e.g.,registration data 1302 and patient survey data 1304 in addition to thequantized health indicator vector 546. For example, some of the risks(such as the IVD degeneration risk, the cardio-pulmonary risk,neurological risk, diabetic risk, and intestinal permeability risk) maybe correlated jointly with the quantized health indicator vector 546 andfamily and individual health history.

Continuing with FIG. 13, various physiological (PHYCO) scores may befurther derived for health screening and assessment. For example, aPHYCO score for predicted risk of musculoskeletal injury 1316 may bederived based on postural PHCI 1306, postural and body alignment score1308, IVD degeneration risk prediction 1310, effective spinal ageprediction 1312 and the patient survey data 1304. For another example, aPHYCO score for predicted risk of cardiologic abnormality, pulmonarydisease, intestinal permeability (i.e., leaky gut syndrome), anddiabetics 1320 may be derived based on corresponding risks 1314.

FIG. 14 further illustrate an exemplary data workflow 1400 for, e.g.,the postural PHCI model to obtain the postural PHCI 1306 of FIG. 13.Those having ordinary skill in the art understand that while theillustration of FIG. 14 is provided in the context of posturalabnormality identification, the underlying principles apply equally tocomputer-aided intelligent identification processes for any other typesof health conditions.

As shown in FIG. 14, in one embodiment, the intelligent PHCI engine 1404may be used to process the quantized health indicator vector 546 and aPHCI matrix 1402 to predict an associated patient health condition (PHC)vector 1403. In some embodiments, the PHCI matrix 1402 may include afirst dimension and a second dimension. The first dimension, forexample, may coincide with the quantized health indicator vector space.The second dimension may represent a vector space comprised ofcomponents that denote a predetermined number of health conditions.Diagnostic conditions may correspond to a subset of patient healthcondition codes. In some embodiments, various components of thequantized health indicator vector 546 may be weighted by the intelligentPHCI engine 1404 differently. The corresponding weights may bedetermined during the training process of the intelligent PHCI engine1404.

An exemplary postural PHCI matrix is illustrated as 1502 in FIG. 15. Thefirst dimension 1506 of the postural PHCI matrix 1502 may representvarious postural deviation components, including but not limited tofront-view postural deviation components 1510 and side-view posturaldeviation components 1512. Other type of postural deviation components,e.g., top-view postural deviation components (not shown in the exampleof 1502) may be included. Vector components other than posturaldeviations (not shown) may also be included in the first dimension 1506.The front-view postural deviation components 1510, for example, mayinclude but are not limited to head shift/tilt, shoulder shift/tilt,underbust shift/tilt, hip shift/tilt, and knee shift/tilt. The side-viewpostural deviation components 1512, for example, may include but are notlimited to head shift, should shift, hip shift, and knew shift. Thetop-view postural deviation components (not shown), for example, mayinclude but are not limited to head, should, underbust, hip, and kneerotations.

The second dimension 1508 of the postural PHCI matrix 1502 in FIG. 15may include a predetermined set of postural health conditions ofinterest (shown as “diagnosis 1”, “diagnosis 2”, . . . , and “diagnosisn”). Health conditions may be selected as a subset of standard posturalabnormal conditions used by, e.g., insurance agencies and hospitals. Forexample, postural conditions may include but are not limited to swayback, scoliosis (right and/or left), trunk shift (left or right),elevated shoulder (left or right), upper cross syndrome, lower crosssyndrome, forward shoulder, forward head, leg length discrepancy, andthe like. The postural conditions may be associated with standard healthdiagnosis codes (ICD9). Other health conditions, detectable in someembodiments that include thermal imager sensors to detect bodytemperature, for example, may be associated with other standard healthdiagnosis codes (ICD10 for inflammation).

The postural PHCI matrix 1502 may be populated with quantized posturaldeviation criteria in the first dimension 1506 for each health conditionin the second dimension 1508, as shown by the values in various cells in1520. Within each cell, a postural deviation that must be present for acorresponding postural condition may be listed. Because quantizedpostural deviation vector components are used, the values list in eachcell may be a collection of discrete deviations. As shown in 1520, forsome postural conditions, multiple postural deviation componentscontribute to particular deviation value calculations. The deviationvalues may all be specified in the corresponding cells. For eachpostural condition, the relationship between different posturaldeviation components may be conjunctive or disjunctive, or a mixture ofconjunctive and disjunctive relationship. The relationship in 1520 maybe default to either conjunctive or disjunctive. The relationship mayalternatively be specified using a separate relationship matrix (notshown).

The PHCI matrix 1502 as well as the relationship or relationship matrixdiscussed above, for example, may be determined using a computer modelbased on one or more machine-learning algorithms.

Continuing with FIG. 15, an exemplary PHC vector 1504 is illustrated inthe context of postural abnormalities. In one embodiment, the PHC vector1504 may be specified as a binary vector (in other words, each componentof the PHC vector comprises a binary value). Specifically, theidentification for each postural abnormal condition may be either “Yes”or “No”, as indicated in various cells of 1504. In some alternativeembodiments, components of the PHC vector 1504 may be comprised ofseveral values or continuous values rather than binary values. Acontinuous value for a component of the PHC vector 1504 may be used, forexample, to represent a probability for a patient to have thecorresponding postural abnormality condition. In another embodiment,each component of the PHC vector 1504 may be one of a predetermined setof category values. For example, the predetermined set of categoryvalues may be high, medium, and low, representing that the risk level aparticular diagnosis component in the PHC vector 1504.

FIG. 16 further illustrates generation of the quantized health indicatorvector 546 of FIG. 14, in the exemplary context of quantized healthindicator vector 546 indicative of various front-view and side-viewpostural deviation components. Actual front-view and side-view deviationcomponents for each individual patient (patient “1” to patient “m”)derived from the normalized 3D body topography data are shown inmeasured postural deviation vectors 1600. A quantization table shown in1602 may be used for quantizing the measured postural deviationcomponents in 1600. The quantization table 1602 may specify, for eachpostural deviation component, the range of actual deviation valuecorresponding to each of a predetermined set of quantization levels (−2,−1, 0, 1, and 2 in this particular example, where negative denotes left,and positive donates right). Quantized postural deviation vector 1604may then be obtained by applying the quantization table 1602 to themeasured postural deviation vectors 1600. Each row in 1604 thusrepresents components of a quantized postural deviation vector (anexample of a particular quantized health indicator vector) for aparticular patient. Each quantized postural deviation vector may be usedas an input quantized health indicator vector 546 in FIG. 14 forobtaining the PHC vector 1403.

FIG. 17 illustrates an exemplary data work flow 1700 for generatingintelligent PPRHA items 1706 using the intelligent PPRHA model andengine 1704 (108 of FIG. 1 and 212 of FIG. 2) based on data collectedand processed by the data collection manager 1702 (211 of FIG. 2,including, for example PHC vector 1403 of FIG. 14 and other data). Inparticular, the output of the data collection manger 1702 may beprocessed by the intelligent PPRHA model 1704 (or 212 of FIG. 2)residing in the intelligent PPRHA engine 108 of FIG. 1 to generate PPRHAitems 1706 (or 216 of FIG. 2).

An example data workflow for the data collection manager 1702 is shownin FIG. 33. Input to the data collection manager 1702 may includepatient data 232, PHC vector 3352, physician data 3354, and device data3356 (such as 3D body topography data). The data collection manager1702, for example, may be configured to handle a variety of tasks. Forexample, the data collection manager 1702 may handle data accesspermission control 3362, data encryption 3364, and Extraction,Translation and Loading (ETL) of data 3366. The data collection manager1702 uses extraction to read data in from multiple data sources,translation to convert the disparate data into one type of format andload to save the formatted data in the system for future retrieval.Device data 3356 may include images 3360 (such as 3D body topographyimages) and other device data 3358. As shown in FIG. 33, the datacollection manager 1702 may be further configured for generating andupdating a dataset for training 3372 and retraining the intelligent PHCImodel or any other intelligent models discussed above. As such, the datacollection manager 1702 may include an annotation function 3368, whichanalyzes various input data, such as PHC vector 3352, physician data3354, device data 3356 (including images 3360 via an image library3370), and performs labeling in the training dataset 3372. The trainingdataset 3372 may be used by a deep learning algorithm 3374 to train theintelligent PHCI Model 212 (or other intelligent models discussedabove).

The Deep Learning algorithm 3374 may be used to automate patternrecognition and identification in the data collected by the datacollector manager 1702, including images 3360 and other device data3358. The PHCI model 212 may comprise a multi-layer feed forwardconvolutional neural network iteratively trained based on a hybrid ofsupervised and unsupervised learning. For example, the PHCI model 212may be trained initially using expert labeled or annotated input datasaved in the image library 3370 to recognize patterns in the expertlabeled input data. The initial supervised learning may include (1)developing a pattern class, a set of patterns with common properties andattributes that are of comparative interest, (2) presenting prototype ortrain input to system, (3) preprocessing data into separate segments(characters, image parts, etc.), (4) extracting key features of data toexpedite pattern recognition, (5) classifying the categories thefeatures belonging to a given pattern, (6) context processing the dataand extracting relevant information pertaining to the data and itsenvironment to increase recognition accuracy.

As collection of data grows while the PHCI model 212 is being used,unsupervised learning may continuously take place. In particular, inorder to perform unsupervised classification, where the pattern is notknown, the system will determine an unknown class by creating it.Additionally, the system will recognize which class to choose whenpattern classes overlap with the primary goal being to choose a classwhile minimizing the error of incorrect categorization.

After the input is compared to all stored examples, a distance matchingscore is calculated. The example with the highest matching score ischosen as the cluster the input will belong to using lateral inhibitionof nodes. The Hamming distance between the exemplar and the input isthen inserted into a vigilance ratio equation. If the ratio exceeds avigilance threshold, the input is considered to be similar enough to theexemplar, and that exemplar is updated by adjusting the weightconnections between it and the input. If the ratio is less than thevigilance threshold for all of the exemplars (the comparison layer), theinput (the recognition layer) is considered unique enough to be its ownnew cluster.

A simplified diagram of an architecture of an initial Deep Learningnetwork 3400 is shown in FIG. 34 including two neural layers (comparisonlayer V and recognition layer R). Neurons of the network 3400 is shownas circles in FIG. 34. Mathematically, the signal of a neuron whichfires is 1, while a neuron which does not fire is 0. The output of therecognition layer neuron, j, is:V_(out)=1 if I_(in)>I_(t)V_(out)=0 if I_(in)<I_(t)

where

${I_{i\; n} = {\sum\limits_{i = 1}^{M}{W_{ij}V_{j}}}},V_{j}$is the input signal to neuron j, the same as the output of the i thcomparison layer neuron (V), and W ij is the weight of the connectionbetween the ith neuron in layer 1 and the jth neuron in layer 2. Thevalue of each ij connection and weight is computed randomly andasynchronously, or in other words in parallel, as a parallel processor.Variables with subscript i vary from 1 to m, while variables withsubscript j vary from 1 to n.

The network 3400 uses feedback between the comparison layer V andrecognition layer R until the output of the first layer after feedbackfrom the second layer is equivalent to the original pattern which wasused as input to the first layer. The degree of this equivalence isdependent on the predetermined vigilance threshold parameter.

Each neuron j in the recognition layer R has a weight vector Wassociated with it. This vector represents a stored pattern for acategory of input patterns. Each neuron j receives as input, the outputof the comparison layer (vector V) via the weight vector W_(j). V_(out)is a step function and will always have a binary value of 1 or 0. Eachneuron j in the comparison layer V will receive input pattern X, a gainsignal (which is 0 or 1), and a feedback signal from the recognitionlayer R (as a weighted sum of the recognition layer outputs. Thefeedback F_(i) through binary weights T_(ji) is:

$F_{i} = {\sum\limits_{j = 1}^{N}{T_{ji}R_{j}}}$

with the comparison layer V having 1 to M neurons and the recognitionlayer R having 1 to N neurons. The vector R j is the output of the jthrecognition layer neuron. Tj is the weight vector from the recognitionlayer R neuron j.

Learning and pattern recognition take place with new “learned”categories of patterns. The tool proceeds with stability usingself-regulating control for competitive learning. Patterns are viewed aspoints within an N-dimensional space. The patterns are clustered basedon proximity of one pattern space to another. A pattern belongs to theclass it is closest to. In some cases the clusters will overlap. For ourexample, a patient may have more than one disease, or some symptoms fromseveral diseases suggesting a new disease.

Competitive learning takes place by creating a standard to make possiblea winner-take-all occurrence. Within a layer, the single node with thelargest value for the set criterion is declared the winner. If two ormore neurons within a layer meet the same largest value, an arbitraryrule, such as select the first, will choose the winner. The input isprocessed with feedback between its two layers until the original inputpattern for the first layer matches the output in the first layer afterfeedback from the second layer. The degree that the input of the firstlayer matches the output of the first layer is called the vigilanceparameter. This predetermined constant is an input variable, for theneural network.

Back to FIG. 17, the intelligent PPRHA model and engine 1704 forms thecore for the generation of PPRHA items. As discussed above, the PPRHAmodel may be trained to extract explicit as well as hidden features andcorrelations into a set of model parameters.

The output PPRHA items 1706 may be a combination of subset of PPRHAitems among a predetermined full set of PPRHA items 1709. PPRHA itemsmay include but are not limited to medicine 1710, supplements 1712,self-directed management 1714, therapeutic exercises 1716,medical/biological tests 1718, referral 1720, and ergonomic prescription1722. A referral 1720, for example, may include referral to generaltypes of medical facilities or clinics (such as therapeutic facilities,orthopedic clinics, emergency rooms, and chiropractic facilities). Areferral 1720 may alternatively or additionally include specificphysicians or practitioners. PPRHA items are not meant to be mutuallyexclusive. For example, therapeutic exercises 1716 (such as physicaltherapies) may be self-directed. In the context of PPRHA for abnormalpostural conditions, the full set of PPRHA items 1709 may includevarious types of physical therapeutic exercises, and a subset ofexercises may be intelligently selected by the PPRHA model and engine1704 for a particular input PHC vector. The intelligent PPRHA mayinclude implementation quantity and frequency for each of the prescribeditem.

For example, the set of physical therapeutic exercises may include butare limited to static hook lying, mermaid, double leg kneeling twist,sacral rolling, standing hip shift, cat-camel, prone dolphin, seatedroll down, floor diamond, rotational kneeling, sitting scapular roll,prone chin tuck, floor angel, wall angel, pectoral muscle stretch,bridge, neck sit up, and plank. In some embodiments, the set ofexercises may include several hundred different types.

In some other embodiments, the PPRHA items 1706 for abnormal posturalconditions may be wholly or partially in the form of ergonomicrecommendations 1722. In particular, the intelligent PPRHA model may betrained to output an ergonomic information that facilitates improvementof postural conditions. Such ergonomic information may be used fordesigning customized apparels, beddings (e.g., including mattresses,sleeping pads, and pillows), braces, support devices, chairs, desks,and/or computing devices/accessories (such as computer keyboard andpointing devices).

The PPRHA model and engine 1704 may further provide prognosis throughdetailed analysis not performed by a physician and thus may provide moreaccurate and new, personalized PPRHA. For example, customized ratherthan standard PPRHA (e.g., number of physical therapy sections) may beprovided to each patient based on data analyzed by the intelligent PPRHAmodel and engine 1704 for the particular patient. For another example, astandard physician prescription (as a form of PPRHA) for body pain afterminimal screening may be an expensive magnetic resonance imaging (MRI)procedure. In addition to costliness, the MRI imaging system's strongmagnetic field can heat up embedded metal and disrupt the activities ofmedical devices, a concern for patients with metal, such as shrapnel,embedded in their bodies, or an implanted medical device, such as anolder pacemaker or a cochlear implant. Alternatively, the intelligentPPRHA model and engine 1704 may analyze patient data and images todetermine an MRI is unnecessary for this patient visit. Further, becausethe PHD can be collected again at any time (e.g., 3D topography data canbe rescanned at any time) and the PPRHA model and engine 1704 evaluatesthe current PHC vector (included in 1702) with respect to historical PHCvectors, the PPRHA engine 1704 may reevaluate whether and when an MRIprocedure should occur, avoiding unnecessary PPRHA testing. In someembodiments, the intelligent PPRHA engine 1704 will include atriage/decision support system to enable at-risk patients to be matchedto the appropriate provider type. The intelligent PPRHA systemdetermines referrals intelligently by selecting the provider type forthe patient and health condition with the highest probability of optimaloutcome. For example, the system may recommend a referral to a primarycare doctor vs. physical therapist vs. emergency room visit vs.orthopedic surgeon vs. pain management doctor for a patient based on thehealth conditions and patient needs from the PPHRA. The medical costsand patient outcomes vary tremendously between visit types, and outcomesare dependent based on the individual characteristics as determined bythe health indicator vector and auxiliary data.

Intelligent referrals may be further enhanced through OutcomeRegistries. Outcome Registries use Patient Recorded Outcome Measurement(PROM)/Medially Validated Questionnaires (MVQ) to assess improvements inthe health of a patient before and after medical treatment. The mainpurpose of outcome reporting is allow insurers to evaluate appropriatepayment based on the value of care. The outcome data can be additionallyused to rate the quality of providers, from which a directory of trustedproviders can be created. Quality providers may be personalized tospecific patient attributes/needs determined by the scanned image dataand auxiliary data. For example, some physical therapists may attain anexcellent rating for knee injury treatment, but a poor rating for spineinjury rehabilitation. The PROMs/MVQs may be collected and integratedinto the machine learning system via the patient portal, or via otherexisting outcome registry databases.

FIG. 18 further illustrates exemplary data workflow for post-diagnosisand post-advocacy monitoring and tracking 1800. In one embodiment, thePPRHA items 1706 generated by the intelligent PPRHA model and engine1704 of FIG. 17 (or 216 of FIG. 2) may be delivered by the portal server102 of FIG. 1 to the physician terminal device (118 of FIG. 1) via thephysician notification process 1802. Automatically generated PPRHA items1706 may be subject to physician modification and approval (1804). Assuch, the physician terminal device 118 may be provided with one or moreapplications for performing notification, modification, approval andother functions. Graphical user interfaces may be provided via theapplications. Exemplary embodiments of the graphical user interfaceswill be shown below with respect to FIGS. 20-27.

The PPRHA approved and/or modified by the physician may then bedelivered to the patient terminal device (122 of FIG. 1) via the portalserver 102 and a patient notification process 1806. The patient terminaldevice may be installed with a monitoring and tracking application whichcommunicates with the portal server and performs functions including butnot limited to a patient monitoring function 1808, a patient surveyfunction 1810, and an educational material provisioning and monitoringfunction 1814. The patient monitoring function 1808, for example, mayprovide tracking of user implementation of the PPRHA items 1706 (e.g.,physical therapeutic exercises prescribed for postural conditions). Thepatient survey function 1810 may provide periodic patient or othervoluntary feedback from the patient describing their body condition andperceived effectiveness of the PPRHA items 1706 as the PPRHA items arebeing implemented. The educational material provisioning and monitoringfunction 1814 may provide educational information about the PPRHA items1706 to help the patient to implement the PPRHA items. For example, demovideos may be provided to the patient to guide the patient through aparticular physical therapeutic exercise as part of the PPRHA item. Theutilization of the educational material may be further tracked by thepatient terminal device and reported to the portal server. Patientmonitoring and tracking functions may be provided on the patientterminal devices via one or more graphical user interfaces. Exemplaryembodiment of the graphical user interfaces will be further describedwith respect to FIGS. 28-30 below.

Continuing with FIG. 18, the post-diagnosis and post-advocacy monitoringmay further include data recollection (e.g., rescan) 1812 of the patientusing any of the distributed data collection devices 104 and 106 ofFIG. 1. Data recollection may be performed periodically or at anyselected time during or after patient implementation of the PPRHA items.The recollected data may be analyzed following data workflow that aresimilar to those used for the original data as illustrated in, e.g.,FIG. 2. The purpose of the data recollection, for example, is foraccessing effectiveness of the PPRHA items 1706 and for improving theintelligent PPRHA model and engine 1704 of FIG. 17.

FIG. 19 illustrates an exemplary embodiment for improving theintelligent PPRHA model 1902 (or 1704 of FIG. 17, or 212 of FIG. 2) viamultiple feedback paths along the arrows indicated by 1920. Multiplefeedback may be weighted using a predetermined set of weight parameters.The multiple feedback paths may include but are not limited to physicianmodification of the PPRHA items 1904, monitored status of patientimplementation and completion of the PPRHA items 1906, patient bodyrescan 1908 (see description above with respect to element 1812 of FIG.18), patient survey 1910, and PHYCO scores 1912, patient survey 1910,patient body rescan 1908, as the data are updated. Data may bemaintained in the data repository 112 with time stamps. Historicalfeedback data, for example, may be used as input, in addition to the PHCvector indicated in FIG. 17 for performing updated training of theintelligent PPRHA model and engine 1902. In some embodiments, theintelligent PPRHA model 1902 may be trained and updated periodicallybased on additional new feedback data that becomes available (to thehistorical data maintained by the data repository 112) after a previoustraining of the intelligent PPRHA model. As such, the intelligent PPRHAmodel 1902 can be updated and improved as it is being used and as moredata is collected and stored in the data repository 112.

In some embodiments, the feedback path via patient monitoring 1906 inFIG. 19 may include but are not limited to tracking of patientimplementation of the PPRHA items (1914) and the tracking of patientutilization of the educational materials (1916) described above. Inparticular, determination as to whether the patient has rigorouslyimplemented the PPRHA items and whether the patient has followed theinstructions from the educational materials may be correlated with andimpact actual effectiveness of the PPRHA Items. This information may betaken into consideration in the training of the intelligent PPRHA model1902 such that the model would be less likely to treat a PPRHA item asineffective when it does not improve patient health at least partiallybecause the patient has not rigorously followed the PPRHA items and/orhas not implemented the PPRHA items correctly.

In some embodiments, the feedback path via the physician modificationtracking 1904 may further include tracking the type of modification madeby the physician. In particular, a physician, after viewing the patientdata and the data automatically provided by the intelligent PPRHA model1902, may decide to modify the PPRHA item in different manners. Forexample, the physician may add or remove a PPRHA component (qualitativemodification 1930). For another example, the physician may keep a PPRHAcomponent, but modify the amount and/or implementation frequency of thePPRHA component (quantitative modification 1932). Different types ofmodification may be recorded by the physician terminal device andreported to the intelligent PPRHA engine (108 of FIG. 1) via the portalserver (102 of FIG. 1). Data sets may be weighted differently forimproving the intelligent PPRHA model 1902 via, e.g., periodicretraining.

FIGS. 20-27 illustrate exemplary graphical user interfaces for thephysician terminal device 118 of FIG. 1 for implementing the variousphysician functions (see, e.g., 1804 of FIG. 18). The graphical userinterfaces may be provided via one or more application(s) running on thephysician terminal device.

FIG. 20, for example, illustrates an exemplary graphical user interfacefor notifying physician of patients where PPRHA items (e.g., therapeuticprograms) require review and approval. The patient list 2004 and theirprograms 2006 may be listed in multiple scrollable pages as shown by2002. Each listed patient in the patient list 2004 and programs 2006 maybe linked to further content for physician review. The physician maychoose to approve one or more or all of the programs using the buttonand check boxes 2008.

FIGS. 21-24 illustrate exemplary graphical user interfaces for physicianreview of patient data. The user interface in FIG. 21, for example,provides a graphical view of patient posture 2102 with various guidinglines 2104 and reference points 2106. FIG. 22, as another example,provides a graphical user interface depicting height/weight (2202), bodyalignment (2204), body shape (2206) data, and general findings by thePHCI engine (2208). FIG. 23, for example, provides a graphical userinterface for the physician to view various postural tilt and shiftcomponents 2302 of the health indicator vector 540 of FIG. 11. In thisexample, results 2305 of various postural tilt and shift components 2302are shown. Column 2304 shows a historical trend of tracked componentsfor the patient. Column 2306 shows individual patient data compared to agroup of patients with respect to each of the postural tilt and shiftcomponents. Historical evolution of postural tilts and shifts of thepatient may also be shown as, e.g., an animation clip 2308 with variousguidelines and reference points, such as 2310 and 2312. FIG. 24 shows agraphical user interface for providing details of other results 2404 forvarious measurements 2402 that may be obtained from the 3D body scandata, with historical trend information 2406 and comparison to others2408.

FIG. 25 illustrates an exemplary graphical user interface for showingPPRHA details for an individual patient. The example is provided in thecontext of physical therapy exercise programs. Column 2502 shows linksto demo videos to particular prescribed exercises 2504. Column 2506shows prescribed quantity for the exercise in terms of number of sets.Column 2508 shows prescribed strenuous level of the exercise in terms ofcompletion duration, number of repetitions, and other quantities foreach exercise set. Column 2510 lists equipment needed for the exercises.The edit button in column 2512 allows the physician to modify theexercise in terms of, e.g., quantity and strenuous level for eachprescribed exercise. Buttons in column 2514 allow the physician toremove one or more of the prescribed exercises.

FIG. 26 illustrates an exemplary graphical user interface for thephysician to add videos listed in 2602 and corresponding exerciseslisted in 2604 using the check boxes 2612 to the PPRHA generated by theintelligent PPRHA model. The physician may further provide quantity2606, duration 2610, and weight-bearings 2608 (if application) for theadded exercises.

FIGS. 27-31 illustrate exemplary graphical user interfaces for thepatient terminal device 122 of FIG. 1 for implementing the variouspatient tracking and monitoring functions (see, e.g., 1808, 1810, and1814 of FIG. 18). For example, FIG. 27 shows graphical user interfacesfor displaying pre-scan survey (2702), for patient notification of 3Dbody scan data availability (2704), for displaying posturalvisualization (2706) to the patient, similar to the graphical userinterfaces for the physician in FIG. 21. FIG. 28, for another example,shows graphical user interface for displaying postural parameters(2802), body shape parameters (2804), and over all postural and IVDscores (2806), similar to the graphical user interface for the physicianshown in FIG. 22. For another example, FIG. 29 shows exemplary graphicaluser interfaces 2902 and 2904 for displaying various postural risks forthe patient in, e.g., high and moderate categories. FIG. 30 furthershows a graphical user interface 3002 for showing a visual ergonomicassessment for the patient and a graphical user interface 3004 forshowing educational material such as videos. For yet another example,FIG. 31 shows a graphical user interface 3102 for tracking patientimplementation of the PPRHA items. For example, the patient may trackexercises using the toggling play/pause button 3106 and complete button3108. The time progression of the exercise may be shown by 3110. Thegraphical user interface 3102 may further show demo video 3112 duringthe exercise. FIG. 31 further shows another exemplary graphical userinterface 3104 for taking patient survey during or after the exercise byproviding selectable survey options 3120 to the user. As discussedabove, the information tracked by the patient terminal devices via thegraphical user interfaces 3102 and 3104 may be recorded and communicatedto the portal server and used by the intelligent PPRHA engine to improvethe PPRHA model.

The computing resources and components for supporting the functioning ofthe various terminals and servers of the system in FIG. 1 may be basedon the computer system 3200 shown in FIG. 32. The computer system 3200may include communication interfaces 3202, system circuitry 3204,input/output (I/O) interfaces 3206, storage 3209, and display circuitry3208 that generates machine interfaces 3210 locally or for remotedisplay, e.g., in a web browser or other applications running on a localor remote machine. The machine interfaces 3210 and the I/O interfaces3206 may include graphical user interfaces (GUIs), touch sensitivedisplays, voice or facial recognition inputs, buttons, switches,speakers and other user interface elements. Additional examples of theI/O interfaces 3206 include microphones, video and still image cameras,headset and microphone input/output jacks, Universal Serial Bus (USB)connectors, memory card slots, and other types of inputs. The I/Ointerfaces 3206 may further include magnetic or optical media interfaces(e.g., a CDROM or DVD drive), serial and parallel bus interfaces, andkeyboard and mouse interfaces.

The communication interfaces 3202 may include wireless transmitters andreceivers (“transceivers”) 3212 and any antennas 3214 used by thetransmitting and receiving circuitry of the transceivers 3212. Thetransceivers 3212 and antennas 3214 may support Wi-Fi networkcommunications, for instance, under any version of IEEE 802.11, e.g.,802.11n or 802.11ac. The communication interfaces 3202 may also includewireline transceivers 3216. The wireline transceivers 3216 may providephysical layer interfaces for any of a wide range of communicationprotocols, such as any type of Ethernet, data over cable serviceinterface specification (DOCSIS), digital subscriber line (DSL),Synchronous Optical Network (SONET), or other protocol.

The storage 3209 may be used to store various initial, intermediate, orfinal datasets or models needed for the intelligent screening, PHCI,PPRHA and monitoring system. The storage 3209 may be centralized ordistributed, and may be local or remote to the computer system 3200.

The system circuitry 3204 may include hardware, software, firmware, orother circuitry in any combination. The system circuitry 3204 may beimplemented, for example, with one or more systems on a chip (SoC),application specific integrated circuits (ASIC), microprocessors,discrete analog and digital circuits, and other circuitry. The systemcircuitry 3204 is part of the implementation of any desiredfunctionality related system 100 of FIG. 1. As just one example, thesystem circuitry 3204 may include one or more instruction processors3218 and memories 3220. The memories 3220 stores, for example, controlinstructions 3226 and an operating system 3224. In one embodiment, theinstruction processors 3218 executes the control instructions 3226 andthe operating system 3224 to carry out any desired functionality relatedto the system 100 of FIG. 1.

Finally, by way of example, FIG. 35 conceptually illustrates a method3500 for automatic and intelligent PHCI and PPRHA. In some embodiments,the method 3500 for automatic and intelligent PHCI and PPRHA isimplemented as one or more software programs, modules, components,plug-ins, or applications which run on at least one processing unit of acomputing device. For example, software that implements the method 3500for automatic and intelligent PHCI and PPRHA may run on a patient healthdata (PHD) collection device (such as PHD collection device 104 or 106described above by reference to FIG. 1), a physician terminal device(such as physician terminal device 118 described above by reference toFIG. 1), a tracking terminal device (such as tracking terminal device122 described above by reference to FIG. 1), the intelligent PHCI engine114 and/or the intelligent PPRHA engine 108 (both described above byreference to FIG. 1), and/or one or more portal server(s) (such asportal server 102 described above by reference to FIG. 1).

In some embodiments, the method 3500 for automatic and intelligent PHCIand PPRHA starts by receiving (at 3502) three-dimensional topographicaldata in a form of a body mesh scan of a target patient. Suchtopographical data may be collected by PHD devices 104 of FIG. 1.

In some embodiments, the method 3500 for automatic and intelligent PHCIand PPRHA then identifies (at 3504) a set of body landmarks of thetarget patient by performing data analytics on the three-dimensionaltopographical data taken from the PHD devices. The data analytics may bebased on image and pattern recognition of particular body segments.Position and/or orientation of these body segments may be indicative ofpostural conditions of the target patient.

Next, the method 3500 for automatic and intelligent PHCI and PPRHAidentifies (at 3506) a set of representations corresponding to the setof body landmarks. For examples, the body landmarks may be representedby a single points, by multiple points, a line, and the like. The set ofrepresentations for the set of body landmark may indicate posturalconditions of the target patient.

Next, the method 3500 for automatic and intelligent PHCI and PPRHAdetermines (at 3508) a vertical reference line and transverse plane formthe 3D topographical data. The vertical reference line and transverseplane may be determined using a force distribution measured by a forceplate in conjunction with the PHD collection device.

In some embodiments, the method 3500 for automatic and intelligent PHCIand PPRHA then predefines (at 3510) a quantized health indicator vectorspace associated with health of the set of body landmarks of the targetpatient. The health indicator vector space may include componentscarrying information relevant to a determination of patient healthconditions. The vector components may be defined as having quantizedvalues.

In some embodiments, the method 3500 for automatic and intelligent PHCIand PPRHA generates (at 3512) a quantized PHCI matrix that associateshealth conditions with quantized values in the quantized healthindicator vector space. After generating the quantized PHCI matrix, themethod 3500 for automatic and intelligent PHCI and PPRHA derives (at3514) a health indicator vector in the quantized health indicator vectorspace based on the set of representations, the vertical reference line,and the transverse plane. For example, the set of representations, thevertical reference line, and the transverse plane may be used to derivevarious postural deviations, which may form the various components ofthe health indicator vector mapped to the health indicator vector space.Next, the method 3500 for automatic and intelligent PHCI and PPRHAquantizes (at 3516) the health indicator vector into the quantizedhealth indicator vector space to obtain a quantized health indicatorvector. After quantization, the method 3500 for automatic andintelligent PHCI and PPRHA stores (at 3518) the quantized healthindicator vector and the 3D topographical data.

In some embodiments, the method 3500 for automatic and intelligent PHCIand PPRHA generates (at 3520) a PHC vector that includes components thatcorrespond to the health conditions based on the quantized healthindicator vector and the PHCI matrix.

After generation of the PHC vector, the method 3500 for automatic andintelligent PHCI and PPRHA executes (at 3522) a PPRHA model that isassociated with a state of training according to a particular machinelearning algorithm and then generates (at 3524) one or more PPRHA itemsby applying or inputting the PHC vector to the PPRHA model. The PPRHAitems may be provided to the physician or patient to follow. Aftergenerating the one or more PPRHA items, the method 3500 for automaticand intelligent PHCI and PPRHA ends.

The methods, devices, processing, circuitry, and logic described abovemay be implemented in many different ways and in many differentcombinations of hardware and software. For example, all or parts of theimplementations may be circuitry that includes an instruction processor,such as a Central Processing Unit (CPU), microcontroller, or amicroprocessor; or as an Application Specific Integrated Circuit (ASIC),Programmable Logic Device (PLD), or Field Programmable Gate Array(FPGA); or as circuitry that includes discrete logic or other circuitcomponents, including analog circuit components, digital circuitcomponents or both; or any combination thereof. The circuitry mayinclude discrete interconnected hardware components or may be combinedon a single integrated circuit die, distributed among multipleintegrated circuit dies, or implemented in a Multiple Chip Module (MCM)of multiple integrated circuit dies in a common package, as examples.

Accordingly, the circuitry may store or access instructions forexecution, or may implement its functionality in hardware alone. Theinstructions may be stored in tangible storage media that is other thana transitory signal, such as a flash memory, a Random Access Memory(RAM), a Read Only Memory (ROM), an Erasable Programmable Read OnlyMemory (EPROM); or on a magnetic or optical disc, such as a Compact DiscRead Only Memory (CDROM), Hard Disk Drive (HDD), or other magnetic oroptical disk; or in or on other machine-readable media. The media may bemade-up of a single (e.g., unitary) storage device, multiple storagedevices, a distributed storage device, or other storage configuration. Aproduct, such as a computer program product, may include storage mediaand instructions stored in or on the media, and the instructions whenexecuted by the circuitry in a device may cause the device to implementany of the processing described above or illustrated in the drawings.

The implementations may be distributed. For instance, the circuitry mayinclude multiple distinct system components, such as multiple processorsand memories, and may span multiple distributed processing systems.Parameters, databases, and other data structures may be separatelystored and managed, may be incorporated into a single memory ordatabase, may be logically and physically organized in many differentways, and may be implemented in many different ways. Exampleimplementations include linked lists, program variables, hash tables,arrays, records (e.g., database records), objects, and implicit storagemechanisms. Instructions may form parts (e.g., subroutines or other codesections) of a single program, may form multiple separate programs, maybe distributed across multiple memories and processors, and may beimplemented in many different ways. Example implementations includestand-alone programs, and as part of a library, such as a shared librarylike a Dynamic Link Library (DLL). The library, for example, may containshared data and one or more shared programs that include instructionsthat perform any of the processing described above or illustrated in thedrawings, when executed by the circuitry.

Various embodiments have been specifically described. However, manyother embodiments are also possible.

What is claimed is:
 1. A system for automatic and intelligent patienthealth condition identification (PHCI) and patient preventive/remedialhealth advocacy (PPRHA), comprising: a data repository; a communicationinterface; and a processing circuitry in communication with the datarepository and the communication interface; wherein the processingcircuitry is configured to: receive 3D topographical data of a targetpatient in a form of body mesh scan from one of a plurality ofdistributed 3D scanners via the communication interface; execute a datasegmentation model trained based on a first machine learning algorithmto automatically identify a predetermined set of body landmarks of thetarget patient and identify a set of representations corresponding tothe predetermined set of body landmarks of the target patient; determinea vertical reference line and a transverse plane from the 3Dtopographical data; predefine a quantized health indicator vector spaceassociated with health of the predetermined set of body landmarks of thetarget patient; associate each of a plurality of predetermined healthconditions with quantized values in the quantized health indicatorvector space to generate a quantized PHCI matrix; derive a healthindicator vector in the quantized health indicator vector space based onthe set of representations, the vertical reference line, and thetransverse plane; quantize the health indicator vector into thequantized health indicator vector space to obtain a quantized healthindicator vector; store the quantized health indicator vector and the 3Dtopographical data in the data repository; automatically generate apatient health condition (PHC) vector comprising a plurality ofcomponent each corresponding to one of the plurality of predeterminedhealth conditions; execute a PPRHA model trained using a second machinelearning algorithm; and automatically generate a PPRHA item by inputtingthe PHC vector into the PPRHA model.
 2. The system of claim 1, wherein:the quantized health indicator vector space comprises dimensionsassociated with tilt deviations and shift deviations in front-view andshift deviations in side-view of the predetermined set of body landmarksof the target patient; and wherein the PHC vector comprises a binary PHCvector comprising dimensions each represented by a binary value.
 3. Thesystem of claim 2, wherein: the predetermined health conditionscomprises a plurality of predetermined posture deviation conditions; andwherein the PPRHA item comprises a physical therapeutic exercise.
 4. Thesystem of claim 2, wherein the set of representations comprisessingle-point coordinates, wherein each of the predetermined set of bodylandmarks of the target patient is associated with one or moresingle-point representations.
 5. The system of claim 4, wherein the tiltdeviations in front-view of a body landmark among the predetermined setof body landmarks of the target patient is determined based on anglesbetween the vertical reference line and a line formed by twosingle-point representations of the body landmark.
 6. The system ofclaim 2, wherein: the predetermined set of body landmarks of the targetpatient comprises head, hips, shoulders, knees, and underbusts; andwherein the tilt deviations and shift deviations in front-view comprisesa head shift, a head tilt, a shoulder shift, a shoulder tilt, anunderbust shift, an underbust tilt, a hip shift, a hip tilt, a kneeshift, and a knee tilt.
 7. The system of claim 1, wherein the set ofrepresentations comprises at least one circumferential attribute.
 8. Thesystem of claim 7, wherein the at least one circumferential attributecomprises circumferences of two body landmarks among the predeterminedset of body landmarks of the target patient, and wherein the quantizedhealth indicator vector space comprises at least a dimensionrepresenting a quantized ratio between the circumferences of the twobody landmarks.
 9. The system of claim 1, further comprising a portalserver for providing a first graphical user interface to a patientterminal device for displaying the PPRHA item to the target patient. 10.The system of claim 1, further comprising a portal server for providinga second graphical user interface to a physician terminal device,wherein: the second graphical user interface is configured to displaythe PPRHA item in the physician terminal device and to enable aphysician to modify the PPRHA item to generate a modified PPRHA item;and wherein the processing circuitry is further configured to record themodified PPRHA item in the data repository.
 11. The system of claim 10,wherein the portal server is further configured to provide a firstgraphical user interface to a patient terminal device for displaying themodified PPRHA item to the target patient.
 12. The system of claim 11,wherein: the PPRHA item comprises a therapeutic exercise and themodified PPRHA item comprises a modified therapeutic exercise; and thefirst graphical user interface is further configured to providemonitoring functions for tracking execution of the modified therapeuticexercise by the target patient.
 13. The system of claim 12, wherein thefirst graphical user interface comprises clickable buttons for thetarget patient to report execution of the modified therapeutic exercise.14. The system of claim 13, wherein the data repository further storesdemo videos and wherein the first graphical user interface is configuredto provide option to select and play demo videos corresponding to themodified therapeutic exercise.
 15. The system of claim 1, furthercomprising a portal server, wherein: the PPRHA model is periodicallyretrained based on updated training data as a result of a plurality offeedbacks; the PPRHA comprises a therapeutic exercise; and the pluralityof feedbacks comprises, with predetermined weights, at least one of:rescan of 3D topographical data of the target patient followingexecution of the therapeutic exercise by the target patient as monitoredby the portal server via a first graphical user interface on a patientterminal device; modification of the therapeutic exercise by a physicianas monitored by the portal server via a second graphical user interfaceon a physician terminal device; self-evaluation following execution ofthe therapeutic exercise by the target patient as collected by theportal server via the first graphical user interface on the patientterminal device; images or videos of the target patient taken by thepatient terminal device while the target patient is executing thetherapeutic exercise as reported to the portal server from the patientterminal device; or records of viewing, by the target patient, demovideos provided to the patient terminal device via the first graphicaluser interface.
 16. A method for automatic and intelligent patienthealth condition identification (PHCI) and patient preventive/remedialhealth advocacy (PPRHA) by a processing circuitry in communication witha data repository and a communication interface, comprising: receiving3D topographical data in a form of body mesh scan of a target patientfrom one of a plurality of distributed 3D scanners via the communicationinterface; executing a data segmentation model trained based on a firstmachine learning algorithm to automatically identify a predetermined setof body landmarks of the target patient and identify a set ofrepresentations corresponding to the predetermined set of body landmarksof the target patient; determining a vertical reference line and atransverse plane from the 3D topographical data; predefining a quantizedhealth indicator vector space associated with health of thepredetermined set of body landmarks of the target patient; associatingeach of a plurality of predetermined health conditions with quantizedvalues in the quantized health indicator vector space to generate aquantized PHCI matrix; deriving a health indicator vector in thequantized health indicator vector space based on the set ofrepresentations, the vertical reference line, and the transverse plane;quantizing the health indicator vector into the quantized healthindicator vector space to obtain a quantized health indicator vector;storing the quantized health indicator vector and the 3D topographicaldata in the data repository; automatically generating a patient healthcondition (PHC) vector comprising a plurality of component eachcorresponding to one of the plurality of predetermined healthconditions; executing a PPRHA model trained using a second machinelearning algorithm; and automatically generating a PPRHA item byinputting the PHC vector into the PPRHA model.
 17. The method of claim16, wherein: the quantized health indicator vector space comprisesdimensions associated with tilt deviations and shift deviations infront-view and shift deviations in side-view of the predetermined set ofbody landmarks of the target patient; the predetermined medicalconditions comprises a plurality of predetermined posture deviationconditions; and the PPRHA item comprises a physical therapeuticexercise.
 18. The method of claim 17, wherein: the set ofrepresentations comprises single-point coordinates, wherein each of thepredetermined set of body landmarks of the target patient is associatedwith one or more single-point representations; and the tilt deviationsin front-view of a body landmark among the predetermined set of bodylandmarks of the target patient is determined based on angles betweenthe vertical reference line and a line formed by two single-pointrepresentations of the body landmark.
 19. The method of claim 16,wherein: the PPRHA model is periodically retrained with updated trainingdata based on a plurality of feedbacks; and the PPRHA item comprises atherapeutic exercise.
 20. The method of claim 19, wherein the pluralityof feedbacks comprises, with predetermined weights, at least one of:rescan of 3D topographical data of the target patient followingexecution of the therapeutic exercise by the target patient as monitoredby a portal server via a first graphical user interface on a patientterminal device; modification of the therapeutic exercise by a physicianas monitored by the portal server via a second graphical user interfaceon a physician terminal device; self-evaluation following execution ofthe therapeutic exercise by the target patient as collected by theportal server via the first graphical user interface on the patientterminal device; images or videos of the target patient taken by thepatient terminal device while the target patient is executing thetherapeutic exercise as reported to the portal server from the patientterminal device; or records of viewing, by the target patient, demovideos provided to the patient terminal device via the first graphicaluser interface.